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ObjectUploadApi.md

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nanonets.ObjectUploadApi

All URIs are relative to https://app.nanonet.com/api/v2

Method HTTP request Description
object_detection_model_upload_file_by_model_id_post POST /ObjectDetection/Model/{model_id}/UploadFile/ Upload training images by File
object_detection_model_upload_urls_by_model_id_post POST /ObjectDetection/Model/{model_id}/UploadUrls/ Upload training images by Url

object_detection_model_upload_file_by_model_id_post

object_detection_model_upload_file_by_model_id_post(data, file, model_id)

Upload training images by File

You can use this endpoint to upload training images for a category (for the specified model) using locally stored files. You will receive model information along with total number of images per category on successful execution.
Note: Filename in Data must be the same as that of the uploaded image name.

Example

from __future__ import print_function
import time
import nanonets
from nanonets.rest import ApiException
from pprint import pprint

# create an instance of the API class
api_instance = nanonets.ObjectUploadApi()
data = 'data_example' # str | 
file = 'file_example' # file | 
model_id = 'model_id_example' # str | 

try:
    # Upload training images by File
    api_instance.object_detection_model_upload_file_by_model_id_post(data, file, model_id)
except ApiException as e:
    print("Exception when calling ObjectUploadApi->object_detection_model_upload_file_by_model_id_post: %s\n" % e)

Parameters

Name Type Description Notes
data str
file file
model_id str

Return type

void (empty response body)

Authorization

No authorization required

HTTP request headers

  • Content-Type: multipart/form-data
  • Accept: application/json

[Back to top] [Back to API list] [Back to Model list] [Back to README]

object_detection_model_upload_urls_by_model_id_post

object_detection_model_upload_urls_by_model_id_post(body, content_type, model_id)

Upload training images by Url

You can use this endpoint to upload training images for a category (for the specified model) by image urls. You can upload multiple images in the same request by adding an array of urls. You will receive model information along with total number of images per category on successful execution.

Example

from __future__ import print_function
import time
import nanonets
from nanonets.rest import ApiException
from pprint import pprint
# Configure HTTP basic authorization: ApiKey
configuration = nanonets.Configuration()
configuration.username = 'YOUR_USERNAME'
configuration.password = 'YOUR_PASSWORD'

# create an instance of the API class
api_instance = nanonets.ObjectUploadApi(nanonets.ApiClient(configuration))
body = nanonets.UploadTrainingImagesByUrlrequest() # UploadTrainingImagesByUrlrequest | 
content_type = 'content_type_example' # str | 
model_id = 'model_id_example' # str | 

try:
    # Upload training images by Url
    api_instance.object_detection_model_upload_urls_by_model_id_post(body, content_type, model_id)
except ApiException as e:
    print("Exception when calling ObjectUploadApi->object_detection_model_upload_urls_by_model_id_post: %s\n" % e)

Parameters

Name Type Description Notes
body UploadTrainingImagesByUrlrequest
content_type str
model_id str

Return type

void (empty response body)

Authorization

ApiKey

HTTP request headers

  • Content-Type: application/json
  • Accept: application/json

[Back to top] [Back to API list] [Back to Model list] [Back to README]